|NO.Z.00049|——————————|BigDataEnd|——|Hadoop&Python.v13|——|Arithmetic.v13|Pandas数据分析库：Pandas分组聚合|

一、分组聚合

### --- 分组聚合

import numpy as np
import pandas as pd
~~~     # 准备数据
df = pd.DataFrame(data = {'sex':np.random.randint(0,2,size = 300),       # 0男，1?
                          'class':np.random.randint(1,9,size = 300),     # 1~8?个班
                          'Python':np.random.randint(0,151,size = 300),  # Python成绩
                          'Keras':np.random.randint(0,151,size =300),    # Keras成绩
                          'Tensorflow':np.random.randint(0,151,size=300),
                          'Java':np.random.randint(0,151,size = 300),
                          'C++':np.random.randint(0,151,size = 300)})
df['sex'] = df['sex'].map({0:'男',1:'?'})                                # 将0，1映射成男?

~~~     # 1、分组->可迭代对象
~~~     # 1.1 先分组再获取数据
g = df.groupby(by = 'sex')[['Python','Java']]                             # 单分组
for name,data in g:
    print('组名：',name)
    print('数据：',data)
df.groupby(by = ['class','sex'])[['Python']]                              # 多分组
~~~     # 1.2 对?列值进?分组
df['Python'].groupby(df['class'])                                         # 单分组
df['Keras'].groupby([df['class'],df['sex']])                              # 多分组

~~~     # 1.3 按数据类型分组
df.groupby(df.dtypes,axis = 1)
~~~     # 1.4 通过字典进?分组
m =
{'sex':'category','class':'category','Python':'IT','Keras':'IT','Tensorflow':'I
T','Java':'IT','C++':'IT'}
for name,data in df.groupby(m,axis = 1):
    print('组名',name)
    print('数据',data)

二、分组聚合

### --- 分组聚合

~~~     # 分组直接调?函数进?聚合
~~~     # 按照性别分组，其他列均值聚合
df.groupby(by = 'sex').mean().round(1) # 保留1位?数
~~~     # 按照班级和性别进?分组，Python、Keras的最?值聚合
df.groupby(by = ['class','sex'])[['Python','Keras']].max()
~~~     # 按照班级和性别进?分组，计数聚合。统计每个班，男??数
df.groupby(by = ['class','sex']).size()
~~~     # 基本描述性统计聚合
df.groupby(by = ['class','sex']).describe()

三、分组聚合apply、transform

### --- 分组后调?apply，transform封装单?函数计算

~~~     # 返回分组结果
df.groupby(by = ['class','sex'])[['Python','Keras']].apply(np.mean).round(1)
def normalization(x):
return (x - x.min())/(x.max() - x.min())                                # 最?值最?值归?化
~~~     # 返回全数据，返回DataFrame.shape和原DataFrame.shape?样。
df.groupby(by = ['class','sex'])
[['Python','Tensorflow']].transform(normalization).round(3)

四、分组聚合agg

### --- agg 多中统计汇总操作

~~~     # 分组后调?agg应?多种统计汇总
df.groupby(by = ['class','sex'])
[['Tensorflow','Keras']].agg([np.max,np.min,pd.Series.count])
~~~     # 分组后不同属性应?多种不同统计汇总
df.groupby(by = ['class','sex'])[['Python','Keras']].agg({'Python':[('最?值',np.max),('最?值',np.min)],
'Keras':[('计数',pd.Series.count),('中位数',np.median)]})

五、透视表pivot_table

### --- 透视表

~~~     # 透视表也是?种分组聚合运算
def count(x):
    return len(x)
df.pivot_table(values=['Python','Keras','Tensorflow'],                   # 要透视分组的值
               index=['class','sex'],                                    # 分组透视指标
               aggfunc={'Python':[('最?值',np.max)],                     # 聚合运算
                        'Keras':[('最?值',np.min),('中位数',np.median)],
                        'Tensorflow':[('最?值',np.min),('平均值',np.mean),('计数',count)]})

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart                                                                                                                                                    ——W.S.Landor

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